pull down the code and run all cells in Bayes_model, Decision_Tree and Logistic_Regression notebooks to run the models and see the outcomes. Run all the cells in Model Analysis to see ROC, Precision score, recall score, F1 score and accuracy of each model.
- Bayes_Model.ipynb - Python notebook by Christine that runs the Bayes classifier
- Decision_Tree.ipynb - Python notebook by Sue that runs the decision tree
- Logistic_Regression.ipynb - Python notebook by Steven that runs the logistic regression
- Model Analysis.ipynb - Python nobebook that generates all analysis data/outcomes for all three models
- data_prep2.py - class that stores all functions used for data processing before it is used in the model. This class is imported in the Bayes_Model, Decision_tree, and Logistic_Regression and Model Analysis notebooks. This also containes the functions used to generate the ROC graphs
- ml_models.py - a script that is used by Model Analysis that contains the code from the model notebooks. the purpose here is to import all three models into a python notebook to generate analysis and graphs used for the presentation and paper.
- Data_prep.ipynb - This is not used, ended up moving what we had here into a script to make it easier to share across multiple notebooks